Gait Data Compression using Linear Prediction Modeling and data decomposition based on discrete wavelet transform

摘要

Gait in the human body measured and expressed in a biomedical signal stored in the patient’s medical record. Gait is a time-series signal, depends on time changes and require a large-enough storage capacity. In this study, an optimization method based on data reduction used to compress gait data using linear prediction modeling. The estimation signal from the method decomposed using discrete wavelet transform (DWT). The estimation signal used to maintain the authenticity of the information in the gait signal. Linear modeling with order value from 8 to 11 generated similar signal with error value up to 1, $67 x10^{-5}$. Daubechies wavelet used to decompose the signal with compression level up to 25.5259%. The results of the research show that the compressed signal has a simpler data size while maintaining the value of the original data. With a smaller capacity, the designed gait database will have more efficient storage space requirements.